Functional Linear Mixed-Effects Model for Actigraphy Data

• Activity Number: 227
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): William Shannon*+ and Jimin Ding and Hong Xian and Tao Li and Carlos Gonzales and Elena Deych
• Companies: Washington University School of Medicine and Washington University in St. Louis and Washington University School of Medicine and Washington University School of Medicine and Washington University School of Medicine and Washington University School of Medicine
• Keywords: actigraphy ; functional data analysis ; functional linear mixed model ; functional principal components analysis ; circadian activity

Abstract

Motivated from the actigraphy data of patients with sleep disorders enrolled in the Medical School of Washington University in Saint Louis, we propose a functional linear mixed-effects model to investigate correlated functional curves. The proposed model is a generalization of 1) functional linear model by including random effects; 2) linear mixed-effects model to functional space. We develop an iterative algorithm to estimate the functional mean curves and decompose the functional variation from between and within subjects. We apply the proposed functional linear mixed effects model to investigate the relationship between the clinical covariates and circadian activity patterns of patients.